Overview

Dataset statistics

Number of variables14
Number of observations26064
Missing cells16050
Missing cells (%)4.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.8 MiB
Average record size in memory112.0 B

Variable types

DateTime1
Numeric12
Categorical1

Alerts

Power (kW) is highly overall correlated with Rear bearing temperature (°C) and 6 other fieldsHigh correlation
Wind direction (°) is highly overall correlated with Nacelle position (°)High correlation
Nacelle position (°) is highly overall correlated with Wind direction (°)High correlation
blade_angle is highly overall correlated with Rear bearing temperature (°C)High correlation
Rear bearing temperature (°C) is highly overall correlated with Power (kW) and 7 other fieldsHigh correlation
Rotor speed (RPM) is highly overall correlated with Power (kW) and 6 other fieldsHigh correlation
Generator RPM (RPM) is highly overall correlated with Power (kW) and 6 other fieldsHigh correlation
Front bearing temperature (°C) is highly overall correlated with Power (kW) and 6 other fieldsHigh correlation
Tower Acceleration X (mm/ss) is highly overall correlated with Power (kW) and 6 other fieldsHigh correlation
Wind speed (m/s) is highly overall correlated with Power (kW) and 6 other fieldsHigh correlation
Tower Acceleration y (mm/ss) is highly overall correlated with Power (kW) and 6 other fieldsHigh correlation
blade_angle has 2244 (8.6%) missing valuesMissing
Rear bearing temperature (°C) has 2244 (8.6%) missing valuesMissing
Nacelle ambient temperature (°C) has 2244 (8.6%) missing valuesMissing
Front bearing temperature (°C) has 2244 (8.6%) missing valuesMissing
Tower Acceleration X (mm/ss) has 2244 (8.6%) missing valuesMissing
Tower Acceleration y (mm/ss) has 2244 (8.6%) missing valuesMissing
Metal particle count counter has 2244 (8.6%) missing valuesMissing
# Date and time has unique valuesUnique
blade_angle has 7713 (29.6%) zerosZeros
Rotor speed (RPM) has 1116 (4.3%) zerosZeros

Reproduction

Analysis started2023-07-08 12:02:20.226970
Analysis finished2023-07-08 12:02:34.551830
Duration14.32 seconds
Software versionydata-profiling v0.0.dev0
Download configurationconfig.json

Variables

Distinct26064
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size203.8 KiB
Minimum2021-01-01 00:00:00
Maximum2021-06-30 23:50:00
2023-07-08T17:32:34.604219image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:32:34.707761image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Power (kW)
Real number (ℝ)

Distinct25885
Distinct (%)99.5%
Missing57
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean586.54969
Minimum-15.71641
Maximum2074.6767
Zeros1
Zeros (%)< 0.1%
Negative3738
Negative (%)14.3%
Memory size203.8 KiB
2023-07-08T17:32:34.812649image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-15.71641
5-th percentile-3.4763557
Q176.232915
median312.61178
Q3905.17785
95-th percentile2012.8625
Maximum2074.6767
Range2090.3931
Interquartile range (IQR)828.94493

Descriptive statistics

Standard deviation650.09986
Coefficient of variation (CV)1.1083458
Kurtosis-0.11734487
Mean586.54969
Median Absolute Deviation (MAD)301.25331
Skewness1.0967148
Sum15254398
Variance422629.83
MonotonicityNot monotonic
2023-07-08T17:32:34.907397image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-1.5 5
 
< 0.1%
-1.620000005 5
 
< 0.1%
-2.950000048 4
 
< 0.1%
-1.539999962 4
 
< 0.1%
-1.580000043 4
 
< 0.1%
-1.419999957 4
 
< 0.1%
-1.629999995 4
 
< 0.1%
-2.019999981 4
 
< 0.1%
-1.50999999 4
 
< 0.1%
-1.110000014 4
 
< 0.1%
Other values (25875) 25965
99.6%
(Missing) 57
 
0.2%
ValueCountFrequency (%)
-15.71640959 1
< 0.1%
-14.61186948 1
< 0.1%
-14.54417365 1
< 0.1%
-13.72473993 1
< 0.1%
-13.71829612 1
< 0.1%
-13.59291015 1
< 0.1%
-13.17220459 1
< 0.1%
-13.14063615 1
< 0.1%
-13.10871906 1
< 0.1%
-13.02244015 1
< 0.1%
ValueCountFrequency (%)
2074.676697 1
< 0.1%
2073.81875 1
< 0.1%
2073.634131 1
< 0.1%
2073.083582 1
< 0.1%
2073.017938 1
< 0.1%
2072.765998 1
< 0.1%
2072.390895 1
< 0.1%
2070.608997 1
< 0.1%
2070.281946 1
< 0.1%
2070.220044 1
< 0.1%

Wind direction (°)
Real number (ℝ)

Distinct25826
Distinct (%)99.3%
Missing57
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean179.04556
Minimum0.026799623
Maximum359.98001
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size203.8 KiB
2023-07-08T17:32:35.003699image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.026799623
5-th percentile19.593003
Q171.609738
median198.91985
Q3264.82019
95-th percentile329.73644
Maximum359.98001
Range359.95321
Interquartile range (IQR)193.21045

Descriptive statistics

Standard deviation103.69617
Coefficient of variation (CV)0.57916083
Kurtosis-1.2939179
Mean179.04556
Median Absolute Deviation (MAD)89.554677
Skewness-0.16368753
Sum4656437.8
Variance10752.896
MonotonicityNot monotonic
2023-07-08T17:32:35.098773image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
41.27000046 4
 
< 0.1%
40.91999817 3
 
< 0.1%
50.11000061 3
 
< 0.1%
34.59999847 3
 
< 0.1%
34.43000031 3
 
< 0.1%
42.72000122 3
 
< 0.1%
37.31000137 3
 
< 0.1%
41.34000015 3
 
< 0.1%
27.54999924 3
 
< 0.1%
53.13999939 3
 
< 0.1%
Other values (25816) 25976
99.7%
(Missing) 57
 
0.2%
ValueCountFrequency (%)
0.02679962305 1
< 0.1%
0.02972330344 1
< 0.1%
0.04288876968 1
< 0.1%
0.04669834521 1
< 0.1%
0.05145375692 1
< 0.1%
0.0535007241 1
< 0.1%
0.1143193258 1
< 0.1%
0.1299999952 1
< 0.1%
0.1599999964 1
< 0.1%
0.1700000018 1
< 0.1%
ValueCountFrequency (%)
359.980011 2
< 0.1%
359.9787147 1
< 0.1%
359.9639333 1
< 0.1%
359.9427708 1
< 0.1%
359.9221752 1
< 0.1%
359.8888171 1
< 0.1%
359.8299225 1
< 0.1%
359.8298555 1
< 0.1%
359.8268278 1
< 0.1%
359.7959098 1
< 0.1%

Nacelle position (°)
Real number (ℝ)

Distinct7214
Distinct (%)27.7%
Missing57
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean179.33645
Minimum0.0038029494
Maximum359.95001
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size203.8 KiB
2023-07-08T17:32:35.202751image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.0038029494
5-th percentile19.873941
Q171.459
median199.08542
Q3265.72765
95-th percentile330.48395
Maximum359.95001
Range359.94621
Interquartile range (IQR)194.26865

Descriptive statistics

Standard deviation103.89494
Coefficient of variation (CV)0.57932972
Kurtosis-1.2970828
Mean179.33645
Median Absolute Deviation (MAD)90.788303
Skewness-0.1656511
Sum4664003.1
Variance10794.158
MonotonicityNot monotonic
2023-07-08T17:32:35.299449image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
267.9224243 158
 
0.6%
38.52999878 96
 
0.4%
227.3127136 87
 
0.3%
264.6297302 79
 
0.3%
196.5805969 79
 
0.3%
41.82509232 75
 
0.3%
69.26449585 75
 
0.3%
262.4346008 75
 
0.3%
263.5316162 73
 
0.3%
142.8009186 72
 
0.3%
Other values (7204) 25138
96.4%
ValueCountFrequency (%)
0.003802949413 1
 
< 0.1%
0.09000000358 1
 
< 0.1%
0.1178577021 7
 
< 0.1%
0.1178588867 3
 
< 0.1%
0.1181895733 1
 
< 0.1%
0.1182251051 15
 
0.1%
0.1183154583 5
 
< 0.1%
0.1183166578 47
0.2%
0.1183173656 4
 
< 0.1%
0.1199999973 3
 
< 0.1%
ValueCountFrequency (%)
359.9500122 1
 
< 0.1%
359.8517633 1
 
< 0.1%
359.400066 1
 
< 0.1%
359.3846459 1
 
< 0.1%
359.3299866 1
 
< 0.1%
359.2943095 1
 
< 0.1%
359.2907683 1
 
< 0.1%
359.2379587 1
 
< 0.1%
359.2099915 1
 
< 0.1%
359.020752 11
< 0.1%

blade_angle
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct9100
Distinct (%)38.2%
Missing2244
Missing (%)8.6%
Infinite0
Infinite (%)0.0%
Mean8.6696473
Minimum0
Maximum92.522224
Zeros7713
Zeros (%)29.6%
Negative0
Negative (%)0.0%
Memory size203.8 KiB
2023-07-08T17:32:35.402986image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.39483333
Q33.3887917
95-th percentile44.996667
Maximum92.522224
Range92.522224
Interquartile range (IQR)3.3887917

Descriptive statistics

Standard deviation19.614168
Coefficient of variation (CV)2.2623951
Kurtosis7.2589693
Mean8.6696473
Median Absolute Deviation (MAD)0.39483333
Skewness2.739623
Sum206511
Variance384.71558
MonotonicityNot monotonic
2023-07-08T17:32:35.500145image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 7713
29.6%
44.99666723 2041
 
7.8%
1.49666667 698
 
2.7%
89.99666595 488
 
1.9%
0.02483333349 395
 
1.5%
0.04966666698 199
 
0.8%
0.07450000048 122
 
0.5%
0.4966666698 109
 
0.4%
92.49666595 80
 
0.3%
1.47166667 79
 
0.3%
Other values (9090) 11896
45.6%
(Missing) 2244
 
8.6%
ValueCountFrequency (%)
0 7713
29.6%
0.0001666666629 7
 
< 0.1%
0.0001754385926 1
 
< 0.1%
0.0003333333259 14
 
0.1%
0.0004999999888 10
 
< 0.1%
0.0006666666518 5
 
< 0.1%
0.0007017543703 1
 
< 0.1%
0.0008333333147 5
 
< 0.1%
0.0009999999776 2
 
< 0.1%
0.001166666641 3
 
< 0.1%
ValueCountFrequency (%)
92.52222443 1
 
< 0.1%
92.52000173 1
 
< 0.1%
92.49666595 80
0.3%
92.46349932 1
 
< 0.1%
92.31466624 1
 
< 0.1%
92.23789402 1
 
< 0.1%
92.21983223 1
 
< 0.1%
92.15866648 1
 
< 0.1%
92.12166595 1
 
< 0.1%
92.06549975 1
 
< 0.1%

Rear bearing temperature (°C)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct19861
Distinct (%)83.4%
Missing2244
Missing (%)8.6%
Infinite0
Infinite (%)0.0%
Mean61.808296
Minimum10.27
Maximum73.4125
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size203.8 KiB
2023-07-08T17:32:35.596784image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum10.27
5-th percentile35.59475
Q160.3275
median66.15
Q368.445469
95-th percentile70.564999
Maximum73.4125
Range63.1425
Interquartile range (IQR)8.1179693

Descriptive statistics

Standard deviation10.984898
Coefficient of variation (CV)0.17772531
Kurtosis3.9425326
Mean61.808296
Median Absolute Deviation (MAD)2.9700001
Skewness-2.0454136
Sum1472273.6
Variance120.66799
MonotonicityNot monotonic
2023-07-08T17:32:35.692064image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
67.46749992 7
 
< 0.1%
67.74749985 6
 
< 0.1%
68.04249992 6
 
< 0.1%
66.57750015 6
 
< 0.1%
68.27749939 6
 
< 0.1%
67.70250053 5
 
< 0.1%
67.41750069 5
 
< 0.1%
67.9875 5
 
< 0.1%
69.63500023 5
 
< 0.1%
67.22500038 5
 
< 0.1%
Other values (19851) 23764
91.2%
(Missing) 2244
 
8.6%
ValueCountFrequency (%)
10.27000008 1
< 0.1%
10.30000019 1
< 0.1%
10.32250009 1
< 0.1%
10.32750006 1
< 0.1%
10.38249974 1
< 0.1%
10.39999962 2
< 0.1%
10.43499975 1
< 0.1%
10.56000023 1
< 0.1%
10.60000038 1
< 0.1%
10.64500012 1
< 0.1%
ValueCountFrequency (%)
73.41249962 1
< 0.1%
73.30999985 1
< 0.1%
73.24250031 1
< 0.1%
73.20750008 1
< 0.1%
73.11500015 1
< 0.1%
73.07749977 1
< 0.1%
72.86999969 1
< 0.1%
72.79749985 1
< 0.1%
72.78684194 1
< 0.1%
72.75277752 1
< 0.1%

Rotor speed (RPM)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct23417
Distinct (%)90.0%
Missing57
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean10.060644
Minimum0
Maximum15.298164
Zeros1116
Zeros (%)4.3%
Negative0
Negative (%)0.0%
Memory size203.8 KiB
2023-07-08T17:32:35.796327image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.10007291
Q19.0142237
median10.111669
Q313.679402
95-th percentile15.160591
Maximum15.298164
Range15.298164
Interquartile range (IQR)4.6651786

Descriptive statistics

Standard deviation4.3959694
Coefficient of variation (CV)0.43694712
Kurtosis0.32595926
Mean10.060644
Median Absolute Deviation (MAD)2.0583314
Skewness-0.99313989
Sum261647.17
Variance19.324547
MonotonicityNot monotonic
2023-07-08T17:32:35.893837image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1116
 
4.3%
8.989999771 188
 
0.7%
9 36
 
0.1%
9.010000229 32
 
0.1%
9.020000458 30
 
0.1%
9.039999962 27
 
0.1%
9.029999733 23
 
0.1%
9.079999924 21
 
0.1%
9.06000042 18
 
0.1%
9.069999695 18
 
0.1%
Other values (23407) 24498
94.0%
(Missing) 57
 
0.2%
ValueCountFrequency (%)
0 1116
4.3%
8.050001634 × 10-51
 
< 0.1%
0.001218000281 1
 
< 0.1%
0.002950000843 1
 
< 0.1%
0.008928000927 1
 
< 0.1%
0.009504001122 1
 
< 0.1%
0.009999999776 1
 
< 0.1%
0.01050000242 7
 
< 0.1%
0.0110000018 6
 
< 0.1%
0.01150000188 5
 
< 0.1%
ValueCountFrequency (%)
15.29816374 1
< 0.1%
15.28837511 1
< 0.1%
15.28808995 1
< 0.1%
15.28667915 1
< 0.1%
15.27377285 1
< 0.1%
15.27341992 1
< 0.1%
15.27253023 1
< 0.1%
15.27202664 1
< 0.1%
15.26989728 1
< 0.1%
15.26868385 1
< 0.1%

Generator RPM (RPM)
Real number (ℝ)

Distinct25817
Distinct (%)99.3%
Missing57
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean1194.4402
Minimum-26.842354
Maximum1813.6926
Zeros13
Zeros (%)< 0.1%
Negative148
Negative (%)0.6%
Memory size203.8 KiB
2023-07-08T17:32:36.150906image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-26.842354
5-th percentile16.780314
Q11071.7416
median1201.295
Q31623.0419
95-th percentile1797.3455
Maximum1813.6926
Range1840.5349
Interquartile range (IQR)551.30034

Descriptive statistics

Standard deviation520.81722
Coefficient of variation (CV)0.43603458
Kurtosis0.33441515
Mean1194.4402
Median Absolute Deviation (MAD)243.27886
Skewness-0.99901323
Sum31063805
Variance271250.58
MonotonicityNot monotonic
2023-07-08T17:32:36.242214image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 13
 
< 0.1%
1070.890015 8
 
< 0.1%
1070.849976 8
 
< 0.1%
1070.900024 7
 
< 0.1%
1070.98999 7
 
< 0.1%
1070.920044 7
 
< 0.1%
1070.969971 7
 
< 0.1%
1070.930054 7
 
< 0.1%
1070.949951 7
 
< 0.1%
1070.910034 6
 
< 0.1%
Other values (25807) 25930
99.5%
(Missing) 57
 
0.2%
ValueCountFrequency (%)
-26.8423537 1
< 0.1%
-0.5083612623 1
< 0.1%
-0.5068035414 1
< 0.1%
-0.4990808093 1
< 0.1%
-0.4937275301 1
< 0.1%
-0.4874308747 1
< 0.1%
-0.4686366921 1
< 0.1%
-0.4620906073 1
< 0.1%
-0.4514498902 1
< 0.1%
-0.292277963 1
< 0.1%
ValueCountFrequency (%)
1813.692566 1
< 0.1%
1811.131451 1
< 0.1%
1811.122547 1
< 0.1%
1811.095705 1
< 0.1%
1810.985557 1
< 0.1%
1810.36381 1
< 0.1%
1810.340572 1
< 0.1%
1810.257826 1
< 0.1%
1809.907642 1
< 0.1%
1809.409035 1
< 0.1%
Distinct18096
Distinct (%)76.0%
Missing2244
Missing (%)8.6%
Infinite0
Infinite (%)0.0%
Mean8.0473337
Minimum-3.63
Maximum27.47
Zeros0
Zeros (%)0.0%
Negative1158
Negative (%)4.4%
Memory size203.8 KiB
2023-07-08T17:32:36.344159image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-3.63
5-th percentile0.065000001
Q14.1199999
median7.5884868
Q310.89
95-th percentile19.287625
Maximum27.47
Range31.1
Interquartile range (IQR)6.7700001

Descriptive statistics

Standard deviation5.5604417
Coefficient of variation (CV)0.69096696
Kurtosis0.22267058
Mean8.0473337
Median Absolute Deviation (MAD)3.393487
Skewness0.62536156
Sum191687.49
Variance30.918512
MonotonicityNot monotonic
2023-07-08T17:32:36.440856image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4.099999905 40
 
0.2%
6.400000095 34
 
0.1%
6.099999905 32
 
0.1%
6 31
 
0.1%
0.8999999762 31
 
0.1%
3.799999952 30
 
0.1%
1.200000048 30
 
0.1%
6.300000191 29
 
0.1%
3.200000048 28
 
0.1%
9.300000191 27
 
0.1%
Other values (18086) 23508
90.2%
(Missing) 2244
 
8.6%
ValueCountFrequency (%)
-3.629999995 1
 
< 0.1%
-3.549999976 1
 
< 0.1%
-3.53157893 1
 
< 0.1%
-3.505263153 1
 
< 0.1%
-3.5 3
< 0.1%
-3.497500002 1
 
< 0.1%
-3.432500064 1
 
< 0.1%
-3.412500083 1
 
< 0.1%
-3.397500086 1
 
< 0.1%
-3.395000076 1
 
< 0.1%
ValueCountFrequency (%)
27.46999998 1
< 0.1%
27.24500008 1
< 0.1%
27.08500013 1
< 0.1%
26.88250008 1
< 0.1%
26.84500027 1
< 0.1%
26.71000004 1
< 0.1%
26.60500011 1
< 0.1%
26.55750027 1
< 0.1%
26.53250027 1
< 0.1%
26.52500019 1
< 0.1%

Front bearing temperature (°C)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct20359
Distinct (%)85.5%
Missing2244
Missing (%)8.6%
Infinite0
Infinite (%)0.0%
Mean63.592418
Minimum10.3
Maximum84.025
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size203.8 KiB
2023-07-08T17:32:36.542717image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum10.3
5-th percentile35.77875
Q158.184375
median68.652786
Q372.762499
95-th percentile74.44
Maximum84.025
Range73.724999
Interquartile range (IQR)14.578124

Descriptive statistics

Standard deviation12.649154
Coefficient of variation (CV)0.19890979
Kurtosis1.7553008
Mean63.592418
Median Absolute Deviation (MAD)4.9722112
Skewness-1.4759622
Sum1514771.4
Variance160.0011
MonotonicityNot monotonic
2023-07-08T17:32:36.639774image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
73.08499947 7
 
< 0.1%
72.98499947 7
 
< 0.1%
72.50999985 6
 
< 0.1%
73.49250031 6
 
< 0.1%
73.875 6
 
< 0.1%
72.89500008 6
 
< 0.1%
73.48250008 6
 
< 0.1%
73.07249985 6
 
< 0.1%
74.1875 5
 
< 0.1%
72.26999931 5
 
< 0.1%
Other values (20349) 23760
91.2%
(Missing) 2244
 
8.6%
ValueCountFrequency (%)
10.30000019 3
< 0.1%
10.38249974 1
 
< 0.1%
10.39999962 4
< 0.1%
10.53000011 1
 
< 0.1%
10.60000038 1
 
< 0.1%
10.66749997 1
 
< 0.1%
10.69999981 1
 
< 0.1%
10.73249993 1
 
< 0.1%
10.80000019 3
< 0.1%
10.9631579 1
 
< 0.1%
ValueCountFrequency (%)
84.02499962 1
< 0.1%
83.8400013 1
< 0.1%
83.60500107 1
< 0.1%
83.37750053 1
< 0.1%
83.37250023 1
< 0.1%
83.28250008 1
< 0.1%
83.21250038 1
< 0.1%
83.18499985 1
< 0.1%
83.03749847 1
< 0.1%
82.8400013 1
< 0.1%

Tower Acceleration X (mm/ss)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct23820
Distinct (%)100.0%
Missing2244
Missing (%)8.6%
Infinite0
Infinite (%)0.0%
Mean62.899644
Minimum1.7278201
Maximum223.24126
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size203.8 KiB
2023-07-08T17:32:36.743898image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1.7278201
5-th percentile3.3679201
Q137.46433
median59.521188
Q387.739519
95-th percentile130.89642
Maximum223.24126
Range221.51344
Interquartile range (IQR)50.27519

Descriptive statistics

Standard deviation38.268912
Coefficient of variation (CV)0.60841222
Kurtosis-0.22187919
Mean62.899644
Median Absolute Deviation (MAD)24.839877
Skewness0.39032576
Sum1498269.5
Variance1464.5096
MonotonicityNot monotonic
2023-07-08T17:32:36.838957image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
22.59617634 1
 
< 0.1%
39.32512574 1
 
< 0.1%
63.74563036 1
 
< 0.1%
31.82580526 1
 
< 0.1%
61.21285982 1
 
< 0.1%
31.48046505 1
 
< 0.1%
54.142906 1
 
< 0.1%
39.59911723 1
 
< 0.1%
63.58062506 1
 
< 0.1%
55.5676199 1
 
< 0.1%
Other values (23810) 23810
91.4%
(Missing) 2244
 
8.6%
ValueCountFrequency (%)
1.727820104 1
< 0.1%
1.942200869 1
< 0.1%
1.945634437 1
< 0.1%
1.953209792 1
< 0.1%
1.955426972 1
< 0.1%
1.966307018 1
< 0.1%
1.969803015 1
< 0.1%
1.993144663 1
< 0.1%
2.071254152 1
< 0.1%
2.085901836 1
< 0.1%
ValueCountFrequency (%)
223.2412621 1
< 0.1%
222.866268 1
< 0.1%
221.9289371 1
< 0.1%
217.1733174 1
< 0.1%
216.4193492 1
< 0.1%
212.877227 1
< 0.1%
205.3702022 1
< 0.1%
204.6637642 1
< 0.1%
204.0840401 1
< 0.1%
202.6945809 1
< 0.1%

Wind speed (m/s)
Real number (ℝ)

Distinct24558
Distinct (%)94.4%
Missing57
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean5.9163947
Minimum0.1534126
Maximum21.456494
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size203.8 KiB
2023-07-08T17:32:36.935322image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.1534126
5-th percentile2.0618477
Q13.8434461
median5.4747564
Q37.5625272
95-th percentile11.311521
Maximum21.456494
Range21.303081
Interquartile range (IQR)3.7190811

Descriptive statistics

Standard deviation2.8271715
Coefficient of variation (CV)0.47785376
Kurtosis0.46589427
Mean5.9163947
Median Absolute Deviation (MAD)1.8233016
Skewness0.74058562
Sum153867.68
Variance7.9928986
MonotonicityNot monotonic
2023-07-08T17:32:37.032435image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5.789999962 11
 
< 0.1%
5.369999886 10
 
< 0.1%
4.900000095 10
 
< 0.1%
5.269999981 9
 
< 0.1%
3.940000057 9
 
< 0.1%
4.699999809 9
 
< 0.1%
4.429999828 8
 
< 0.1%
5.059999943 8
 
< 0.1%
7.550000191 8
 
< 0.1%
5.010000229 8
 
< 0.1%
Other values (24548) 25917
99.4%
(Missing) 57
 
0.2%
ValueCountFrequency (%)
0.1534125978 1
< 0.1%
0.217331608 1
< 0.1%
0.218231453 1
< 0.1%
0.2331002884 1
< 0.1%
0.2456646264 1
< 0.1%
0.2564251497 1
< 0.1%
0.2860689349 1
< 0.1%
0.3013500445 1
< 0.1%
0.3192680769 1
< 0.1%
0.3321002871 1
< 0.1%
ValueCountFrequency (%)
21.45649366 1
< 0.1%
21.01492853 1
< 0.1%
20.37379866 1
< 0.1%
20.32031411 1
< 0.1%
20.30840321 1
< 0.1%
20.07488661 1
< 0.1%
19.70247615 1
< 0.1%
19.49005656 1
< 0.1%
19.47865925 1
< 0.1%
19.46470032 1
< 0.1%

Tower Acceleration y (mm/ss)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct23820
Distinct (%)100.0%
Missing2244
Missing (%)8.6%
Infinite0
Infinite (%)0.0%
Mean27.243895
Minimum1.7510436
Maximum149.67504
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size203.8 KiB
2023-07-08T17:32:37.131557image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1.7510436
5-th percentile3.3341609
Q116.028561
median23.603127
Q336.338566
95-th percentile61.115087
Maximum149.67504
Range147.924
Interquartile range (IQR)20.310005

Descriptive statistics

Standard deviation17.423565
Coefficient of variation (CV)0.63954016
Kurtosis1.5967135
Mean27.243895
Median Absolute Deviation (MAD)9.7007257
Skewness1.0430737
Sum648949.57
Variance303.5806
MonotonicityNot monotonic
2023-07-08T17:32:37.227391image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10.28735064 1
 
< 0.1%
16.94487838 1
 
< 0.1%
23.79099665 1
 
< 0.1%
17.22223105 1
 
< 0.1%
25.70797145 1
 
< 0.1%
19.67439306 1
 
< 0.1%
19.85276275 1
 
< 0.1%
30.75970325 1
 
< 0.1%
22.94221454 1
 
< 0.1%
33.22994347 1
 
< 0.1%
Other values (23810) 23810
91.4%
(Missing) 2244
 
8.6%
ValueCountFrequency (%)
1.751043613 1
< 0.1%
1.854043037 1
< 0.1%
2.028895257 1
< 0.1%
2.035457241 1
< 0.1%
2.041154361 1
< 0.1%
2.053697926 1
< 0.1%
2.131821811 1
< 0.1%
2.137553095 1
< 0.1%
2.139793236 1
< 0.1%
2.142283609 1
< 0.1%
ValueCountFrequency (%)
149.6750391 1
< 0.1%
145.6273895 1
< 0.1%
134.3644958 1
< 0.1%
133.4145958 1
< 0.1%
131.4149296 1
< 0.1%
126.3254455 1
< 0.1%
126.0633268 1
< 0.1%
125.6462119 1
< 0.1%
123.667559 1
< 0.1%
120.890539 1
< 0.1%
Distinct5
Distinct (%)< 0.1%
Missing2244
Missing (%)8.6%
Memory size203.8 KiB
429.0
11983 
430.0
5354 
431.0
4151 
428.0
1479 
427.0
 
853

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters119100
Distinct characters9
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row427.0
2nd row427.0
3rd row427.0
4th row427.0
5th row427.0

Common Values

ValueCountFrequency (%)
429.0 11983
46.0%
430.0 5354
20.5%
431.0 4151
 
15.9%
428.0 1479
 
5.7%
427.0 853
 
3.3%
(Missing) 2244
 
8.6%

Length

2023-07-08T17:32:37.319523image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-08T17:32:37.411444image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
429.0 11983
50.3%
430.0 5354
22.5%
431.0 4151
 
17.4%
428.0 1479
 
6.2%
427.0 853
 
3.6%

Most occurring characters

ValueCountFrequency (%)
0 29174
24.5%
4 23820
20.0%
. 23820
20.0%
2 14315
12.0%
9 11983
10.1%
3 9505
 
8.0%
1 4151
 
3.5%
8 1479
 
1.2%
7 853
 
0.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 95280
80.0%
Other Punctuation 23820
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 29174
30.6%
4 23820
25.0%
2 14315
15.0%
9 11983
12.6%
3 9505
 
10.0%
1 4151
 
4.4%
8 1479
 
1.6%
7 853
 
0.9%
Other Punctuation
ValueCountFrequency (%)
. 23820
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 119100
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 29174
24.5%
4 23820
20.0%
. 23820
20.0%
2 14315
12.0%
9 11983
10.1%
3 9505
 
8.0%
1 4151
 
3.5%
8 1479
 
1.2%
7 853
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 119100
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 29174
24.5%
4 23820
20.0%
. 23820
20.0%
2 14315
12.0%
9 11983
10.1%
3 9505
 
8.0%
1 4151
 
3.5%
8 1479
 
1.2%
7 853
 
0.7%

Interactions

2023-07-08T17:32:32.928694image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:32:21.213316image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:32:22.286116image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:32:23.316242image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:32:24.347247image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:32:25.352012image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:32:26.410219image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:32:27.607151image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:32:28.652704image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:32:29.707317image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:32:30.782032image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:32:31.796389image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:32:33.008551image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:32:21.296544image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:32:22.364785image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:32:23.396411image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:32:24.428544image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:32:25.434372image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:32:26.491225image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:32:27.686224image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:32:28.734526image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:32:29.789968image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:32:30.862908image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:32:31.874009image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:32:33.096308image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:32:21.385130image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:32:22.453373image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:32:23.486189image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:32:24.516261image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:32:25.524595image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:32:26.584704image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:32:27.776868image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:32:28.824917image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:32:29.883248image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:32:30.949936image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:32:31.959598image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:32:33.186940image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:32:21.556847image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:32:22.540398image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:32:23.573955image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:32:24.603495image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:32:25.615328image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:32:26.675976image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:32:27.865902image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:32:28.917102image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:32:29.976190image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:32:31.039463image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:32:32.045607image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:32:33.265556image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:32:21.630039image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:32:22.621356image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:32:23.654371image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:32:24.679183image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:32:25.696136image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:32:26.756937image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:32:27.947200image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:32:28.998341image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:32:30.058975image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:32:31.118139image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:32:32.123138image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:32:33.354181image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:32:21.713212image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:32:22.707623image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:32:23.741410image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:32:24.763019image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:32:25.787130image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:32:26.846554image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:32:28.035933image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:32:29.088535image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:32:30.147132image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:32:31.202688image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:32:32.206236image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:32:33.445815image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:32:21.799229image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:32:22.800030image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:32:23.831976image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:32:24.853261image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:32:25.882080image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:32:26.937109image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:32:28.129372image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:32:29.180113image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:32:30.242526image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:32:31.293666image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:32:32.295405image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:32:33.536399image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:32:21.883201image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:32:22.887461image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:32:23.921853image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:32:24.939099image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:32:25.972365image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:32:27.172003image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:32:28.216686image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:32:29.272159image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:32:30.333984image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:32:31.380102image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:32:32.378624image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:32:33.624473image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:32:21.964182image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:32:22.975513image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:32:24.007397image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:32:25.023675image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:32:26.062326image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:32:27.259562image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:32:28.303571image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:32:29.358448image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:32:30.424111image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:32:31.466336image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:32:32.461194image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:32:33.717281image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:32:22.050467image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:32:23.065041image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:32:24.098421image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:32:25.110143image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:32:26.152633image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:32:27.350327image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:32:28.397622image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:32:29.450738image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:32:30.518303image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:32:31.554173image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:32:32.546094image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:32:33.798674image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:32:22.125767image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:32:23.145914image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:32:24.178196image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:32:25.187150image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:32:26.235903image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:32:27.434574image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:32:28.479789image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:32:29.534605image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:32:30.602838image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:32:31.630405image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:32:32.620751image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:32:33.881138image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:32:22.200597image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:32:23.226073image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:32:24.259329image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:32:25.265983image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:32:26.317850image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:32:27.515860image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:32:28.562877image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:32:29.616160image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:32:30.688281image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:32:31.710078image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:32:32.847033image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Correlations

2023-07-08T17:32:37.486157image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Power (kW)Wind direction (°)Nacelle position (°)blade_angleRear bearing temperature (°C)Rotor speed (RPM)Generator RPM (RPM)Nacelle ambient temperature (°C)Front bearing temperature (°C)Tower Acceleration X (mm/ss)Wind speed (m/s)Tower Acceleration y (mm/ss)Metal particle count counter
Power (kW)1.0000.0470.041-0.3960.7530.9820.982-0.1240.9390.6430.9640.8050.193
Wind direction (°)0.0471.0000.901-0.0770.0950.0510.0490.1220.0950.1000.0520.1040.229
Nacelle position (°)0.0410.9011.000-0.0670.0800.0450.0430.1180.0840.0880.0450.0950.224
blade_angle-0.396-0.077-0.0671.000-0.567-0.388-0.3900.198-0.452-0.203-0.349-0.2050.114
Rear bearing temperature (°C)0.7530.0950.080-0.5671.0000.7490.7470.0270.8340.5130.7120.5790.215
Rotor speed (RPM)0.9820.0510.045-0.3880.7491.0000.999-0.1180.9350.6810.9450.8290.191
Generator RPM (RPM)0.9820.0490.043-0.3900.7470.9991.000-0.1260.9350.6790.9460.8280.192
Nacelle ambient temperature (°C)-0.1240.1220.1180.1980.027-0.118-0.1261.000-0.1140.047-0.150-0.0040.421
Front bearing temperature (°C)0.9390.0950.084-0.4520.8340.9350.935-0.1141.0000.6010.9020.7500.238
Tower Acceleration X (mm/ss)0.6430.1000.088-0.2030.5130.6810.6790.0470.6011.0000.5830.8830.159
Wind speed (m/s)0.9640.0520.045-0.3490.7120.9450.946-0.1500.9020.5831.0000.7640.187
Tower Acceleration y (mm/ss)0.8050.1040.095-0.2050.5790.8290.828-0.0040.7500.8830.7641.0000.163
Metal particle count counter0.1930.2290.2240.1140.2150.1910.1920.4210.2380.1590.1870.1631.000

Missing values

2023-07-08T17:32:34.009114image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2023-07-08T17:32:34.199198image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-07-08T17:32:34.401818image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

# Date and timePower (kW)Wind direction (°)Nacelle position (°)blade_angleRear bearing temperature (°C)Rotor speed (RPM)Generator RPM (RPM)Nacelle ambient temperature (°C)Front bearing temperature (°C)Tower Acceleration X (mm/ss)Wind speed (m/s)Tower Acceleration y (mm/ss)Metal particle count counter
02021-01-01 00:00:00368.901584304.503192292.0688480.065.50749910.4728391244.1109371.01000066.51249822.5961765.60803510.287351427.0
12021-01-01 00:10:00392.556922303.958125292.0688480.066.25500010.6372091263.5932850.61250067.82749934.8329095.65652014.871085427.0
22021-01-01 00:20:00343.706641302.359788292.0688480.065.78250010.2117781215.010403-0.15250067.40500140.5807335.53076015.981503427.0
32021-01-01 00:30:00332.474633305.378038292.0688480.066.26000010.1563031206.525427-0.49000067.91000055.7245715.17829523.814625427.0
42021-01-01 00:40:00314.817380296.362417292.0688480.065.7175019.9675061184.718527-0.44000067.21750137.8243235.74513817.453197427.0
52021-01-01 00:50:00362.447025289.427405292.0688480.066.45750010.4198631238.703558-0.29500067.89999939.2753345.86802116.190434427.0
62021-01-01 01:00:00342.113658293.516806292.0688480.066.36764710.2162841212.217692-0.11470667.95588143.7235505.91809315.102848427.0
72021-01-01 01:10:00327.701140297.012696292.0688480.066.47750010.1002631202.949028-0.03000068.07250045.1562275.57497518.735163427.0
82021-01-01 01:20:00306.155627295.243520292.0688480.066.1250019.9124211177.437166-0.23500067.49750042.7724905.83845116.270514427.0
92021-01-01 01:30:00237.921848299.820375292.0688480.065.5550009.2986981104.892221-0.17500066.66000139.3475265.25781623.131583427.0
# Date and timePower (kW)Wind direction (°)Nacelle position (°)blade_angleRear bearing temperature (°C)Rotor speed (RPM)Generator RPM (RPM)Nacelle ambient temperature (°C)Front bearing temperature (°C)Tower Acceleration X (mm/ss)Wind speed (m/s)Tower Acceleration y (mm/ss)Metal particle count counter
260542021-06-30 22:20:0015.86000035.16000031.950001NaNNaN8.991070.989990NaNNaNNaN3.05NaNNaN
260552021-06-30 22:30:0011.03000034.32000031.950001NaNNaN8.991070.859985NaNNaNNaN3.04NaNNaN
260562021-06-30 22:40:00-8.62000036.22000131.950001NaNNaN8.991070.920044NaNNaNNaN2.45NaNNaN
260572021-06-30 22:50:001.77000037.75999831.950001NaNNaN8.991070.969971NaNNaNNaN2.88NaNNaN
260582021-06-30 23:00:0050.68000036.50000031.950001NaNNaN8.991071.099976NaNNaNNaN3.61NaNNaN
260592021-06-30 23:10:0081.54000142.91999831.950001NaNNaN8.991070.969971NaNNaNNaN3.75NaNNaN
260602021-06-30 23:20:0095.98999843.86000131.950001NaNNaN8.991070.849976NaNNaNNaN3.93NaNNaN
260612021-06-30 23:30:0076.29000142.52999931.950001NaNNaN8.991070.930054NaNNaNNaN3.83NaNNaN
260622021-06-30 23:40:0042.88999937.70000131.950001NaNNaN8.991070.729980NaNNaNNaN3.46NaNNaN
260632021-06-30 23:50:008.19000039.70999931.950001NaNNaN8.991070.969971NaNNaNNaN2.86NaNNaN